Evaluation of Interpretable Deep Learning Approaches for Neuroimaging in Dementia using Stroke Classification as a Ground Truth for Brain Pathology

这项研究的背景包括潜在影响的简要说明痴呆症和阿尔茨海默病是2018年主要的死亡原因，英国的患病率不断增长，预计到2025年将上升到100万人。深度学习模型在帮助早期诊断和预后方面显示出巨大的前景。然而，它们的复杂性和缺乏透明度阻碍了它们应用于临床实践的可能性。在风险很高的临床工作中，这尤其是一个问题，误诊的后果可能危及生命。在这些领域，可解释的方法可以提供一种方法来评估网络的行为，并经常将其可视化，以更好地理解为什么会获得特定的结果。然而，由于缺乏验证和缺乏基础事实，最近的方法受到了限制。本项目利用快速发展的可解释ML领域的最新技术，在将替代的可解释方法应用于神经退行性疾病和痴呆之前，比较不同的可解释方法参考中风等神经疾病的情况。目的与目的本研究旨在以中风分类作为脑病理的基础事实，对几种解释方法在痴呆诊断和预后方面的有效性进行批判性验证。首先，我们的目标是建立一个网络，能够以近乎完美的准确性将中风患者与对照组进行分类，以确保注意力地图是有用的和信息丰富的。这将使我们能够探索这些模型对站点和/或数据质量、体素维度和数据标准化的普适性。我们将考虑的其他技术考虑包括病变大小或萎缩的影响，以及临床特征对模型性能和可解释性的影响。在使用中风的基本事实案例来评估可解释性方法后，最优方法将应用于博士阶段的痴呆症。从这些模型中产生的注意图将由具有痴呆专业知识的放射科医生(来自伦敦大学学院和合作网站)的样本进行定性和半定量的临床实用评估。研究方法的新颖性可解释的医学成像，可解释的人工智能的一个子领域，是翻译研究的一个重要主题。计划中的痴呆症神经图像可解释性研究将是新颖的，并提供队列和患者层面的注意图，这对于提供治疗痴呆症的精确药物至关重要。据我们所知，使用笔划作为基本事实的可解释性方法的比较评估在以前还没有报道。这项研究包括与放射科医生密切合作，提供临床专业知识和临床实用数据，使其成为一个真正的跨学科转换项目。与EPSRC的战略和研究领域保持一致我们的研究重点与EPSRC的战略保持一致，以实现更早和更有效的诊断(1)，并使用机器学习从临床数据和图像中整合更多信息(3)。此外，与临床医生的合作与与相关利益相关者的密切接触的重点一致(7)。这项工作属于人工智能技术以及图像和视觉计算研究领域。参与该项目的任何公司或合作者都将与伦敦大学学院痴呆症研究中心(DRC)和伦敦大学学院医院(UCLH)的NIHR生物医学研究中心合作。

Brief description of the context of the research including potential impactDementia and Alzheimer's disease were the leading causes of death in 2018, with a growing prevalence in the UK that is expected to rise to 1 million by 2025. Deep learning models have shown great promise in aiding early diagnosis and prognosis. However, their complexity and lack of transparency hinders the likelihood of their adoption into clinical practice. This is particularly a problem in clinical routines where stakes are high, and the consequences of misdiagnosis can be life-threatening. In these areas interpretable approaches can provide a way of assessing, and often visualising, the behaviour of the network to better understand why a particular outcome is obtained. However, recent approaches have been limited due to lack of validation and the absence of a ground truth.By taking advantages of the latest technologies from the rapidly developing field of interpretable ML, this project aims to compare alternative interpretability methods in reference to 'ground truth' neurological conditions like stroke before applying them to neurodegenerative disease and dementia.Aims and ObjectivesThis research aims to critically validate the effectiveness of several interpretation methods for dementia diagnosis and prognosis using stroke classification as a ground truth for brain pathology. To begin with, we will aim to build a network that can classify stroke patients from controls with near-perfect accuracy in order to ensure that the attention maps are useful and informative. This will allow us to explore the generalisability of these models to site and/or data quality, voxel dimension and data normalisation. Other technical considerations we will consider include the effect of lesion size or atrophy, and clinical characteristics on model performance and interpretability. Having used the ground-truth case of stroke to assess interpretability approaches, the optimal approaches will then be applied to dementia duringthe PhD phase. The resulting attention maps from these models will be assessed qualitatively and semi-quantitatively for clinical utility by a sample of radiologists (from UCL and collaborating sites) with expertise in dementia.Novelty of Research MethodologyInterpretable medical imaging, a subfield of 'explainable AI', is an important topic for translational research. The planned research on neuroimage interpretability in dementia will be novel and provide both cohort and patient level attention maps, essential for delivering precision medicine for dementia. To our knowledge, comparative assessment of interpretability methods using stroke as a ground truth has not previously been reported. This research includes working closely with radiologists, to provide clinical expertise and data on clinical utility, making it a truly interdisciplinary translational project.Alignment to EPSRC's strategies and research areasOur research focus aligns with EPSRC's strategy for enabling earlier and more effective diagnosis (1) and integration of additional information from clinical data and images using machine learning (3). In addition, collaborating with clinicians aligns with the focus in strong engagement with relevant stakeholders (7). This work falls within the artificial intelligence technologies and image and vision computing research areas.Any companies or collaborators involvedThe project involves collaboration with the UCL Dementia Research Centre (DRC) and the NIHR Biomedical Research Centre at University College London Hospital (UCLH).